scholarly journals Genetic evaluation of partial growth trajectory of Santa Inês breed using random regression models

2010 ◽  
Vol 39 (5) ◽  
pp. 1029-1036 ◽  
Author(s):  
Kassiana Adriano Pinto de Oliveira ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Legendre Polynomials ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Growth Trajectory ◽  
Random Regression ◽  
Data Set ◽  
Average Growth ◽  
Santa Inês ◽  
Group Sex ◽  
Different Order

It was evaluated data set of 19,303 weight records of Santa Inês sheep in order to evaluate distinct polynomial functions with different order for better adjustements of fixed and random regressions of growth trajectory and to estimate (co)variances components and genetic parameters of this trajectory. Fixed effects used in analysis were contemporary group, sex and birth type. Ordinary and Legendre polynomials, ranging from two to four orders, were evaluated for fixed regression of average growth trajectory. Legendre and quadratic b-spline functions, ranging from three to four orders, were evaluated for random regressions. Legendre polynomials of order fourth were suitable to fit random regression, while ordinary polynomials of third order were the best for fixed trajectory. Direct heritabilities on days 1, 50, 150, 250 and 411 were 0.24, 0.12, 0.44, 0.84, and 0.96, respectively, while maternal heritabilities for the same ages were 0.24, 0.19, 0.09, 0.02, and 0.01, respectively. Genetic correlations among weights in subsequent ages were high, tending to unity, and there were negative correlations between weights at early ages and weights at late ages. It is possible to modify the growth trajectory by selection with the observed genetic variability. Genetic control of weights at initial ages is not the same in late ages. So, selection of animals for slaughter in early age must be different from that of replacement animals.

scholarly journals Genetic relationship between milk dry matter and other milk traits in extended lactations of Polish Holstein cows

2010 ◽  
Vol 55 (No. 3) ◽  
pp. 91-104 ◽  
Author(s):  
K. Yazgan ◽  
J. Makulska ◽  
A. Węglarz ◽  
E. Ptak ◽  
M. Gierdziewicz
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Fixed Effects ◽  
Dry Matter ◽  
Genetic Correlations ◽  
Random Regression ◽  
Holstein Cows ◽  
Milk Traits ◽  
Data Set ◽  
The Third

The objective of this research was to examine heritabilities and genetic, phenotypic and permanent environmental relationships between milk dry matter (DM) and milk traits such as milk, fat, protein and lactose yields, milk urea nitrogen (MUN) and somatic cell score (SCS) in extended (to 395 days) lactations of Holstein cows from a big farm in Poland. The data set consisted of 78 059 test day records from the first, second and third lactations of 3 792 cows, daughters of 210 sires and 1 677 dams. Single- or two-trait random regression models were used with fixed effects of calving year, calving month, dry period and calving interval and random additive genetic and permanent environmental effects. The last two fixed effects were not included in the analysis of first lactation data. The highest values of heritabilities for all traits, except DM, were observed in the second lactation. First lactation heritabilities for all traits – except milk yield and SCS – were smaller than those in the third lactation. Lactose yield was highly heritable, with average h<SUP>2</SUP> equal to 0.25, 0.29 and 0.28 in lactations 1, 2 and 3, respectively. Heritability for DM was slightly lower than that for lactose (0.22, 0.26 and 0.28 for lactations 1, 2 and 3, respectively). In all lactations heritabilities for SCS were below 0.1. Genetic correlations between DM and milk yield (0.64–0.74) were lower than those between MUN and milk yield (0.67–0.79) as well as between lactose and milk yield (0.72–0.82). In general, DM was much more closely correlated with fat or protein yield (0.55–0.79) than with MUN or lactose (0.38–0.76). Only in the third lactation the correlation between DM and protein (0.72) was lower than between lactose and protein (0.76). For all lactations there were very high genetic correlations between DM and lactose (0.96–0.98) and high correlations between DM and MUN (0.63–0.83) and between lactose and MUN (0.70–0.85). The results suggest that further research is needed, focused on DM and its relationship with other traits in larger populations. &nbsp;

scholarly journals Random regression models using different functions to estimate genetic parameters for milk production in Holstein Friesians

2016 ◽  
Vol 46 (9) ◽  
pp. 1649-1655
Author(s):  
Mariana de Almeida Dornelles ◽  
Paulo Roberto Nogara Rorato ◽  
Luis Telo Lavadinho da Gama ◽  
Fernanda Cristina Breda ◽  
Carlos Bondan ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomials ◽  
Fixed Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Residual Variance ◽  
Lower Estimation ◽  
The One ◽  
Residual Variances

ABSTRACT: The objective of this study was to compare the functions of Wilmink and Ali and Schaeffer with Legendre polynomials in random regression models using heterogeneous residual variances for modeling genetic parameters during the first lactation in the Holstein Friesian breed. Five thousand eight hundred and eighty biweekly records of test-day milk production were used. The models included the fixed effects of group of contemporaries and cow age at calving as covariable. Statistical criteria indicated that the WF.33_HE2, LEG.33_HE2, and LEG.55_HE4 functions best described the changes in the variances that occur throughout lactation. Heritability estimates using WF.33_HE2 and LEG.33_HE2 models were similar, ranging from 0.31 to 0.50. The LEG.55_HE4 model diverged from these models, with higher estimates at the beginning of lactation and lower estimates after the 16th fortnight. The LEG55_HE4, among the three better models indicated by the index, is the one with highest number of parameters (14 vs 34) and resulted in lower estimation of residual variance at the beginning and at the end of lactation, but overestimated heritability in the first fortnight and presented a greater difficulty to model genetic and permanent environment correlations among controls. Random regression models that used the Wilmink and Legendre polynomials functions with two residual variance classes appropriately described the genetic variation during lactation of Holstein Friesians reared in Rio Grande do Sul.

scholarly journals Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine

2019 ◽  
Vol 97 (9) ◽  
pp. 3669-3683 ◽  
Author(s):  
Piush Khanal ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Kent Gray ◽  
Francesco Tiezzi
Keyword(s):  
Meat Quality ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Carcass Composition ◽  
Additive Genetic Effect ◽  
Yield Traits ◽  
Data Set ◽  
Crossbred Pigs

Abstract Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.

scholarly journals Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials

2008 ◽  
Vol 37 (4) ◽  
pp. 602-608 ◽  
Author(s):  
Claudio Napolis Costa ◽  
Claudio Manoel Rodrigues de Melo ◽  
Irineu Umberto Packer ◽  
Ary Ferreira de Freitas ◽  
Nilson Milagres Teixeira ◽  
...  
Keyword(s):  
Variance Components ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Holstein Cows ◽  
Residual Variance ◽  
Environmental Variance ◽  
Environmental Correlations ◽  
Lp Estimates

Data comprising 263,390 test-day (TD) records of 32,448 first parity cows calving in 467 herds between 1991 and 2001 from the Brazilian Holstein Association were used to estimate genetic and permanent environmental variance components in a random regression animal model using Legendre polynomials (LP) of order three to five by REML. Residual variance was assumed to be constant in all or in some classes of lactation periods for each LP. Estimates of genetic and permanent environmental variances did not show any trend due to the increase in the LP order. Residual variance decreased as the order of LP increased when it was assumed constant, and it was highest at the beginning of lactation and relatively constant in mid lactation when assumed to vary between classes. The range for the estimates of heritability (0.27 - 0.42) was similar for all models and was higher in mid lactation. There were only slight differences between the models in both genetic and permanent environmental correlations. Genetic correlations decreased for near unity between adjacent days to values as low as 0.24 between early and late lactation. A five parameter LP to model both genetic and permanent environmental effects and assuming a homogeneous residual variance would be a parsimonious option to fit TD yields of Holstein cows in Brazil.

Effect of milk fat to protein ratio on genetic variance for milk yield in Thai tropical Holstein cattle

2016 ◽  
Vol 96 (3) ◽  
pp. 410-415 ◽  
Author(s):  
S. Puangdee ◽  
M. Duangjinda ◽  
W. Boonkum ◽  
S. Buaban ◽  
S. Katawatin
Keyword(s):  
Milk Fat ◽  
Variance Components ◽  
Legendre Polynomials ◽  
Bayesian Method ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Random Regression ◽  
Third Order ◽  
Protein Ratio ◽  
Regression Test

The objective of this research was to investigate the optimum fat to protein ratio (FPR) in Thai tropical Holstein dairy cattle. First parity data consisting of 20 492 milk yields (MY) records for 24 891 cows for the period 2001 and 2011, were used in the analysis. The analysis used a random regression test-day animal model of third-order Legendre polynomials through the creation of a covariance function based on different FPRs. Variance components were estimated using the Bayesian method via the Gibbs sampling. The estimated heritability of MY in relation to FPR ranged from 0.19 to 0.27 with the pattern being similar to the genetic variances. Genetic correlations of MY at different FPRs were high at consecutive FPRs and then declined to negative in response to greater differences in FPR. Based on the results, it is concluded that the optimum FPR is in the range of 0.9 to 1.9, corresponding to the genetically controlled energy balance for MY in tropical Holsteins.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

scholarly journals Selection of a suitable data set and model for the estimation of genetic parameters of the weaning weight in beef cattle

2007 ◽  
Vol 50 (6) ◽  
pp. 562-574
Author(s):  
L. Vostrý ◽  
J. Přibyl ◽  
Z. Veselá ◽  
V. Jakubec
Keyword(s):  
Beef Cattle ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Suitable Model ◽  
Data Set ◽  
Weaning Weight ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for weaning weight of beef cattle in the Czech Republic. Nine subsets were tested for the selection of a suitable subset. The subsets differed from each other in the limit of sampling criteria. The most suitable subset satisfied these conditions: at least 5 individuals per each sire, 5 individuals per HYS (herd, year, season), 2 sires per HYS, and individuals per dams that have at least one half-sister and two offspring (n = 4 806). The selection of a suitable model was carried out from 10 models. These models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent maternal environment effect, HYS, sire × herd or sire × year interaction, and some of the fixed effects: dam’s age, sex (young bull, heifer × single, twin born), HYS, year, herd. The direct heritability (h2a) ranged from 0.06 to 0.17, of maternal heritability (h2m) from 0.03 to 0.06. The genetic correlations between the direct and maternal effect (ram) were in the range of –0.15 –0.42.

33 Impact of Including the Cause of Missing Records on Genetic Evaluations for Growth in Commercial Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 18-18
Author(s):  
Mary Kate Hollifield ◽  
Daniela Lourenco ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Jeremy T Howard ◽  
...  
Keyword(s):  
Linear Model ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Pedigree Information ◽  
Linear Regression Method ◽  
Classical Models ◽  
Group Sex ◽  
Genetic Evaluations ◽  
Pearson Correlations ◽  
Crossbred Pigs

Abstract It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

scholarly journals Components of (co)variance of the days open in Siboney dairy cows

2010 ◽  
Vol 62 (1) ◽  
pp. 136-143 ◽  
Author(s):  
D. González-Peña ◽  
J.L. Espinoza-Villavicencio ◽  
D. Guerra ◽  
A. Palacios ◽  
J.C. Évora ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Genetic Correlations ◽  
Random Regression ◽  
Residual Variance ◽  
Parity Number ◽  
Days Open ◽  
Univariate Model

The records of 63,406 calvings of Siboney dairy cows (5/8 Holstein 3/8 Cuban Zebu) were used to estimate the components of covariance of the days open (DO). Five models were used: of repeatability; univariate; bivariate; of random regression with Legendre polynomials and the parity number as predicting variable; and a model of random regression with Legendre polynomials and heterogeneity of the residual variance. The heritability obtained with the univariate model was 0.09 in the first calving and decreased to 0.05 in the fifth. A higher estimate of heritability (0.12) was obtained with the repeatability model. When the model of random regression with heterogeneity of the residual variance was used, the heritability was higher than the values estimated with the previous models. The genetic correlations among the DO in different calvings, estimated with the models of random regression with and without heterogeneity of the residual variance, were close to 1.0. It is concluded that the estimates of heritability increased with the use of the random regression models. The genetic correlations among the DO of different calvings indicated that in the first three, the DO are regulated for the most part by the same genes.

scholarly journals Estimation of genetic parameters for female fertility traits in the Czech Holstein population

2019 ◽  
Vol 64 (No. 5) ◽  
pp. 199-206 ◽  
Author(s):  
Michaela Brzáková ◽  
Ludmila Zavadilová ◽  
Josef Přibyl ◽  
Petr Pešek ◽  
Eva Kašná ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Reproductive Traits ◽  
Additive Genetic Effect ◽  
Data Set ◽  
Age At First Calving ◽  
Fertility Traits ◽  
Holstein Population

Genetic parameters for fertility traits in Czech Holstein population were estimated. The database obtained from the Czech-Moravian Breeders Corporation with 6 414 486 insemination records between years 2005–2015 was used. Date of calving of the selected animals was taken from the database of milk records from 2005–2015. Fertility traits were age at first service (AFS), age at first calving (AFC), days open (DO), calving interval (CI) and first service to conception interval in cows (FSC-C) and heifers (FSC-H). The heritability of each trait was estimated using single-trait animal models. The model included fixed effects of herd-year-season of birth, herd-year-month of calving, lactation order, parity, last calving ease, linear and quadratic regressions on age at first insemination in heifers or on age at first calving in cows. Random effects were animal, permanent environmental effect and random residual error. After edits, the final data set included up to 599 901 observations from up to 448 037 animals dependent on traits. The range of heritability estimates was from 0.010 to 0.058. The lowest heritability was for first service to conception interval in heifers, and the highest heritability was for age at first service. Variances of random permanent effects were higher than variance of additive genetic effect in all traits manifested in mature cows. Repeatability ranged from 0.060 to 0.090. Genetic correlations between traits were estimated using a bivariate animal model. High positive genetic correlations were found between AFS–AFC, DO–CI, FSC-C–DO and FSC-C–CI. A moderate genetic correlation was found between AFS–FSC-H and between AFC. A negative correlation was found between AFS–FSC-C. Correlations between other traits were close to zero. The results suggest that the level of these reproductive traits can be improved by selection of animals with high genetic merit.

Sign in / Sign up

Export Citation Format

Share Document